知鸦日报2021-10-08

This is the third post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a look at Part 1 (Decision Making at Netflix) and Part 2 (What is an A/B Test?). Subsequent posts will go into more details on experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

In Part 2: What is an A/B Test we talked about testing the Top 10 lists on Netflix, and how the primary decision metric for this test was a measure of member satisfaction with Netflix. If a test like this shows a statistically significant improvement in the primary decision metric, the feature is a strong candidate for a roll out to all of our members. But how do we know if we’ve made the right decision, given the results of the test? It’s important to acknowledge that no approach to decision making can entirely eliminate uncertainty and the possibility of making mistakes. Using a framework based on hypothesis generation, A/B testing, and statistical analysis allows us to carefully quantify uncertainties, and understand the probabilities of making different types of mistakes.

Quality of a client application is of paramount importance to global digital products, as it is the primary way customers interact with a brand. At Netflix, we have significant investments in ensuring new versions of our applications are well tested. However, Netflix is available for streaming on thousands of types of devices and it is powered by hundreds of micro-services which are deployed independently, making it extremely challenging to comprehensively test internally. Hence, it became important to supplement our release decisions with strong evidence received from the field during the update process.

Our team was formed to mine health signals from the field to quickly evaluate new versions of the client applications. As we invested in systems to enable this vision, it led to increased development velocity, which arguably led to better development practices and quality of the applications. The goal of this blog post is to highlight the investment areas for this vision and the challenges we are facing today.

本文主要是写给 Egg 团队这些深度参与过早期社区建设的同学，需要翻新下我们的认知了。

代码滚滚，App 的开发码工们带着他们的历史包袱，迎接这新技术、新硬件的到来，本文打算聊聊 5G 来了后，“传统”的移动应用开发中的坑。

atom 编辑器新增一个 teletype 的功能，可以实现多人在线编辑代码。效果看起来挺炫酷，想了解一下是怎么实现的，于是研究了一下。

抽象一下文本文档的协同编辑这个问题，就是同步多个设备之间的操作合并，最后都能达到最终一致的结果。现在解决文本文档的协同编辑有两种方案，一种是 Google Doc 使用的 Operational transformation (OT)，还有一种就是 Atom teletype 使用的 Conflict-free replicated data type (CRDT)。

今天准备谈下MDM主数据系统设计的一些关键点，涉及到元数据管理，对象建模，数据质量管理，数据集成和数据服务能力开放等。

Dropbox Capture is a new visual communication tool designed to make it easy for teams to asynchronously share their work using screen recordings, video messages, screenshots, or GIFs. There's no formal onboarding required, and you can start sharing your ideas in seconds. In fact, simplicity is key to the Capture experience, and it's a value that also extends down to the development of Capture’s underlying code.